The completion of the human genome sequence has led to an immense amount of information on the structure of the human genome. We now face the challenge of using this rich information to improve human health through a better understanding of common human diseases. This challenge is intensified by complex genetic and environmental mechanisms that contribute to common complex diseases. Our long term goals are to develop state-of-the-art statistical and quantitative methods to analyze genetic association studies in order to extract the maximum amount of genetic information. Our short term goals are to develop new "model-free" statistical methods for genetic association studies, in order to provide robust, yet powerful, methods as we gain knowledge on the genetic and environmental factors that lead to disease and response to treatment. By taking this strategy, we anticipate that our proposed research plans will succeed at providing the research community with the much-needed statistical methods to evaluate the association of large scale genomic variation with complex human traits. Our planned specific aims are to: 1) Develop Nonparametric Statistical Methods For Genetic Association Studies: Based on our recent developments of a new class of nonparametric statistics, we plan to extend our methods to be more powerful for detecting gene-gene interactions, to create "scan" statistics for genome-wide analyses, and to account for a variety of traits;2) Develop New Genomic Scan Statistics: To consider a large number of candidate genes, or a genome-wide association study, we plan to develop new scan statistics for evaluating the association of haplotypes with a variety of traits;3) Develop Nonparametric Statistical Methods and Scan Statistics for Pedigree Data: Aims 1-2 are focused on unrelated subjects, and we plan to extend these methods to pedigree data;4) Develop User-Friendly Software and Documentation: We plan to provide, at no charge to the scientific community, user-friendly software that implements our methods, including well-documented procedures and examples on their usage;5) Apply New Methods to Collaborative Research Studies: All methods developed in Aims 1-4 will be applied to ongoing collaborative studies in order to gain insights to their strengths and weaknesses, and to provide potential clinical benefits for our collaborative studies. Translational Potential: Our research plans address a critically important scientific and clinical problem--to make optimal use of large-scale genomic information to evaluate its role in human health. Through development of new quantitative methods, our research has the potential to improve the diagnosis, prognosis, and treatment of complex genetic human diseases, as well as other human traits.